On the Predictability of the Equity Premium Using Deep Learning Techniques

Abstract

Deep learning is drawing keen attention in contemporary financial research. In this article, the authors investigate the statistical predictive power and economic significance of financial stock market data by using deep learning techniques. In particular, the authors use the equity premium as the response variable and financial variables as predictors. The deep learning techniques used in this study provide useful evidence of statistical predictability and economic significance. Considering the statistical predictive performance of the deep learning models, H2O deep learning (H2ODL) gives the smallest mean-squared forecast error (MSFE), with the corresponding highest cumulative return (CR) and Sharpe ratio (SR) in each of the out-of-sample periods. Specifically, the H2ODL with Rectifier used as the activation function outperformed the other models in this article. In the fusion results, the SAE-with-H2O using the Maxout activation function yields the smallest MSFE with the corresponding highest CR and SR in all of the out-of-sample periods. It is worth noting that the higher the CR, the higher the SR and the lower the MSFE, which concords with a rule of thumb. Overall, the empirical analysis in this study revealed that the SAE-with-H2O using the Maxout activation function produced the best statistically predictive and economically significant results with robustness across all out-of-sample periods. TOPICS: Big data/machine learning, performance measurement, quantitative methods, simulations, statistical methods Key Findings ▪ In this article, the authors use deep learning models to predict the equity premium, employing a plethora of well-known predictors. ▪ The authors employ deep learning models such as deep neural networks, a stacked autoencoder, and long short-term memory models. ▪ The statistical and economic significance of the proposed models is examined and back tested in three out-of-sample periods.